From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)

@article{Ferro2018FromET,
  title={From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)},
  author={N. Ferro and N. Fuhr and G. Grefenstette and J. Konstan and P. Castells and E. Daly and Thierry Declerck and Michael D. Ekstrand and Werner Geyer and J. Gonzalo and T. Kuflik and Krister Lind{\'e}n and B. Magnini and Jian-Yun Nie and R. Perego and Bracha Shapira and I. Soboroff and N. Tintarev and Karin M. Verspoor and M. Willemsen and J. Zobel},
  journal={Dagstuhl Manifestos},
  year={2018},
  volume={7},
  pages={96-139}
}
We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the… Expand
Offline evaluation options for recommender systems
Evaluating Multimedia and Language Tasks
Using Collection Shards to Study Retrieval Performance Effect Sizes
The Information Retrieval Group at the University of Duisburg-Essen
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